Impact of trap-related non-idealities on the performance of a novel TFET-based biosensor with dual doping-less tunneling junction

This article presents a novel dielectric-modulated biosensor based on a tunneling field-effect transistor. It comprises a dual doping-less tunneling junction that lies above an n+ drain region. By employing the wet-etching technique, two cavities are carved in the gate dielectric, and with the entry of various biomolecules into the cavities, the electrostatic integrity of the gate changes, accordingly. Numerical simulations, carried out by the Silvaco ATLAS device simulator, show that including trap-assisted tunneling significantly modulate the biosensor's main parameters, such as on-state current, subthreshold swing, and transconductance and their corresponding sensitivities. We also evaluate the effect of semi-filled cavities on our proposed biosensor’s performance with various configurations. The FOMs like Ion/Ioff = 2.04 × 106, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${S}_{{I}_{ds}}$$\end{document}SIds=1.48 × 105, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${S}_{SS}$$\end{document}SSS=0.61 in the presence of TAT show that our proposed biosensor has a promising performance.

Nowadays, the need for medical diagnostic equipment capable of rapidly detecting of newly-emerging viruses has increased tremendously. Biosensors, which can detect a vast range of diseases at the early stages, are among the most popular and most interesting equipment. Biosensors are basically categorized to label detection and label free, depends on their detection mechanism. Unlike label detection biosensors, label free devices can be more accurate and prevent unwanted side effects 1 . Their ability in detecting the neutral and charged biomolecules with high sensitivity is a significant advantage compared to other biosensors, such as ion-sensitive devices 2,3 .
In 4 authors have stated that biosensors based on tunneling field-effect transistors (TFETs) are a better choice than those based on MOSFETs. This was attributed to the smaller response time and lower leakage of TFET biosensors. In recent years, various types of TFET-based biosensors with different architectures such as core-shell nanotubes 5 , vertical [6][7][8] and bilayer 9 structures, have been proposed. In 2008, Hueting et al. proposed the first charge plasma-based diode in which metals with appropriate work functions induced the electrons and holes in an intrinsic semiconductor instead of using dopants 10 . The mentioned technique can be a viable solution for dopant-related problems in nanoscale transistors 11 . In 2013, Kumar and Janardhanan suggested the first silicon-based doping-less TFET, which paved the path for developing this idea 12 14 . In 2022, we proposed the first doping-less biosensor based on the cladding layer concept in which a highly-doped semiconductor acts as an inductive metal in the source region, and the drain current sensitivity of 6.17 × 10 5 at V GS = 0.4 V was obtained 15 . While trap-assisted tunneling is expected to cause lower problems in the doping-less TFET compared with the doping-based ones, their negative effects on the performance of biosensors should not be neglected. In this paper, we propose a novel biosensor which benefits from a doping-less tunneling junction that is built over an n + -drain region. Our paper's first aim is to assess our biosensor's performance and investigate its reliability in the presence of TAT .

Device structure and simulation methodology
In Fig. 1, a cross-sectional view of our Dielectric-Modulated Dual doping-less Source TFET-based (DMDS-TFET) biosensor is depicted. In our device, which benefits from two source regions, carriers tunnel to a U-shape channel and then move toward the drain side, which is located at the bottom of the biosensor. To convert this TFET to a biosensor, two cavities with the dimension of 5 nm × 20 nm are carved in the gate dielectric using the wet-etching technique 16 . The channel length and thickness are 50 nm and 10 nm, respectively. The work function of the gate metal is 4.3 eV, while Platinum, with the work function of 5.93 eV, induces holes in the source region. Although the tunneling interface of our biosensor is intrinsic, the drain region is n + -doped with a concentration of 3 × 10 18 . Due to using silicon and SiO 2 in the design of this biosensor, its fabrication process is fully compatible with CMOS technology. To prevent gate-leakage current 1 nm distance between the cavities and the channel is devised 17 . We also use a 20 nm distance (T iso ) between the gate and source metals.
Although the structure of the proposed biosensor geometrically seems a little complex, but from the employed materials point of view, it is compatible with conventional CMOS technology. According to Fig. 2 we propose a multi-step fabrication process to realize DMDS-TFET. It commences with the epitaxial growth of n + silicon next to an intrinsic silicon layer (see Fig. 2a). The selective etch technique is employed to create a U-shaped trench in the intrinsic silicon, followed by the deposition of SiO 2 in the U-shaped trench, which acts as the gate spacer (see Fig. 2b). In the next step, another trench is created in the gate spacer, which is filled with the gate metal using the deposition technique (see Fig. 2c). Then, two formed trenches are filled with SiO 2 (see Fig. 2d). The other trenches are created in the spacer regions for the deposition of source metals (see Fig. 2e). Finally, the wet etching technique is employed to shape two cavities in the channel (see Fig. 2f).
In Fig. 3a, we have drawn the extracted values of the transfer characteristics of ref 18 alongside our reproduced results, and a good matching is obtained for all bias points. Since our device simulator does not have an appropriate carrier-induced bandgap narrowing model, we chose another TFET based on the charge plasma concept 12 for calibration, too (see Fig. 3b). A reasonable match between the original and the regenerated sets of data indicates that our following reported performance evaluations are reasonably valid and reliable.
Silvaco ATLAS device simulator was employed for the simulation of our proposed biosensor 19 . Due to the higher accuracy of the dynamic non-local BTBT model compared with local models, we used this model for calculating the on-state current. We have also activated auger, SRH, CVT, fermi, and drift-diffusion models for

Simulation results
The impact of various biomolecules on the energy bands diagrams of DMDS-TFET biosensor along the A-B cutline (drawn on Fig. 1) are illustrated in Fig. 4. According to this figure, when air is replaced with 3-aminopropyltriethoxysilane (APTES with k = 3.57) or Gelatin (with k = 12) biomolecules in the cavities the band-to-band tunneling distance (d BTBT ) reduces. Such a reduction is mainly attributed to the impact of permittivity of the biomolecules on the electric field strength at the tunneling junction. When the cavities are filled with Gelatin, we have the lowest d BTBT , which means that the intensity of the electric field at the source-channel junction is significantly higher. The equation that shows the dependence of tunneling probability on different parameters of the device is given by 18 www.nature.com/scientificreports/ where m* is the effective mass of charge carrier, E g is bandgap, ℏ is the reduced plank constant, ΔΦ is the energy overlap of the tunneling window, ε C and t C are the channel dielectric constant and thickness, t ox is the dielectric thickness (in this work comprised of SiO 2 and cavity thicknesses), and ε ox is the dielectric constant. The exponential dependence of the tunneling probability on the dielectric constant of biomolecules indicates that biomolecules with higher dielectric constants remarkably enhance tunneling probability, resulting in higher on-state current. Figure 5 compares the transfer characteristics of DMDS-TFET biosensor at the presence of different biomolecules with and without undesirable TAT conduction mechanism. Comparing these two figures shows that including TAT model in the simulations increases the biosensor's off-state current. Furthermore, TAT significantly modulates the onset voltage of tunneling. While in Fig. 5a, there is a distinct boundary between the offstate and on-state even for k = 1, in Fig. 5b, the gradual increase of the drain current makes it difficult to clearly distinguish these states except for k = 8 and k = 12. This means that excluding TAT from the simulations can lead to more ideal but unrealistic results.
Drain current sensitivity is one of the main merit factors in the performance assessment of FET-based sensor. It is given by where I air ds is the drain current of the device with air-filled cavities and I bio ds is the drain current in the presence of biomolecules with k values higher than the air 21 . Figure 6a,b illustrates the drain current sensitivity for various biomolecules with and without TAT. It can be seen that including TAT in the simulations reduce the S I ds of the biosensor. Moreover, TAT Shifts the S I ds.max point to the higher gate voltages. According to the left figure, for k = 12, we have S I ds.max = 1.53 × 10 6 at V GS = 0.9 V, while on the other hand and for the same k, TAT degrades the S I ds.max to 2.94 × 10 5 at V GS = 1. www.nature.com/scientificreports/ The negative impact of trap states that facilitate undesired tunneling of charge carriers from the source valance band to the channel conduction band is undeniable. One the manifestations of this phenomenon is the change in the steepness of the device switching. In order to study the switching behavior of the TFET sensor, we compare the subthreshold swing of the device, with and without TAT, in Fig. 7a. It can be inferred that including TAT dramatically decreases the subthreshold swing for all values of k. For example, there is 142.1 mV/dec difference between the values of subthreshold swing without and with TAT for k = 1. While this difference reaches 51.29 mV/dec for k = 12. Subthreshold swing sensitivity is defined by where SS air and SS bio are the subthreshold swing of the device with air-filled and biomolecule-filled cavities, respectively 21 . One interesting point is that, unlike the S I ds , in this case, the values of S SS in the presence of TAT are higher than the values of S SS when TAT is neglected (see Fig. 7b). This is mainly originated from the wide differences among the subthreshold swings when the TAT model is activated.
The selectivity of our biosensor is also investigated by calculating the selectivity factor between APTES-Biotin, and Biotin-Uricase, respectively, according to the following equations 22   www.nature.com/scientificreports/ of TAT. Both figures depict that our biosensor is more capable of distinguishing between Biotin and Uricase than APTES and Biotin. This can be attributed to the relative difference between the dielectric constant of two biomolecules which is 0.7 for the former and 0.35 for the latter. One distinguishing feature of FET-based biosensors is their ability to detect charged biomolecules in addition to neutral biomolecules. In this section, we evaluate our biosensor's performance in detecting DNA biomolecules (with k = 6). Figure 9 shows the energy bands diagram at the tunneling junction along the A-B cutline (as depicted in Fig. 1). The figure indicates that positively charged biomolecule forms a steeper tunneling junction which can decrease band-to-band tunneling distance at the source-channel junction, while negatively charged biomolecule degrades band bending at the tunneling junction, leading to a higher d BTBT . Figure 10a,b compares the transfer characteristics of positively and negatively charged DNA biomolecules without and with TAT. In Fig. 10a, we have a much-steeper switching and lower values of V onset (the gate voltage at which BTBT starts). While by taking TAT into account V onset increases considerably. For example, there is a ΔV onset of 0.23 V for k = 6 and N f = 1 × 10 12 (C cm −2 ) between the two cases.
In Fig. 11a,b, the drain current sensitivity ( S I ds ) of DMDS-TFET for charged DNA biomolecule without and with TAT is demonstrated. In the case of positively charged DNA with N f = 1 × 10 12 (C cm −2 ) and without TAT, the S I ds.max can be as much as S I ds.max for k = 12. In contrast, this value for negatively charged DNA with N f = − 1 × 10 12 (C cm −2 ) is almost the same as that for k = 3.57. As depicted in Fig. 11b, activating TAT reduces the S I ds considerably. Interestingly, similar to the previous case, with including TAT in the simulations, the values of S I ds.max for positively charged DNA with N f = 1 × 10 12 (C cm −2 ) are close to the S I D for k = 8. In comparison, the values of S I ds.max for negatively charged DNA with N f = − 1 × 10 12 (C cm −2 ) are marginally similar to that for k = 3.57. Figure 12a shows the impact of trap-assisted tunneling on the subthreshold swing of DMDS-TFET at the presence of positively and negatively charged DNA biomolecules. Similar to the neutral biomolecules, activating TAT in the simulation degrades subthreshold swing considerably. It can be observed that the SS value for k = 6 and N f = 1 × 10 12 (C cm −2 ) is 87.2 mV/dec, and with activating TAT, the value with 154% increase reaches  www.nature.com/scientificreports/ 135.1 mV/dec. In Fig. 12b, the subthreshold swing sensitivity ( S SS ) of the biosensor for charged DNA with and without TAT activation is plotted. Like the neutral biomolecules, the higher values of S SS in the presence of TAT are mainly attributed to the wide differences among the subthreshold swings when the TAT model is activated. For k = 6 and N f = 1 × 10 12 (C cm −2 ), The S SS = 0.58 and excluding TAT decreases the value to S SS = 0.51. A practical biosensor should have high linearity and small distortion. Reduction of device linearity can lead to the degradation of signal-to-noise performance, which decrease biosensors sensitivity. Calculating transconductance is one way to assess these parameters 23 . Figure 13a shows the impact of TAT on the transconductance of DMDS-TFET biosensor. It can be seen that higher values of k contribute to higher transconductance. To attain more realistic results, TAT is also activated for this graph. In Fig. 13b, the transconductance sensitivity for different values of k is plotted. It is formulated as where S g m.air is the value of the transconductance for k = 1 and S g m.bio is the value of the transconductance at the presence of biomolecule.
In Fig. 14 we investigate the sensor stability in the presence of temperature change. Temperature change is an important non-ideality that can considerably degrade the TFET-based biosensors performance stability. The impact of 100 K increment in the temperature for Gelatin (with k = 12), which has the highest off-state current compared to the other biomolecules, is evaluated in the figure. It can clearly be seen that in both figures subthreshold region is more affected. This is mainly because the band-to-band tunneling equation has no direct dependency on temperature.
In Table 1 we compare the performance of some recently reported dielectric-modulated biosensors. For this purpose, the Gelatin biomolecule (with k = 12) is chosen, and the biosensors' threshold voltage sensitivity and off-state current sensitivity are compared. The table indicates that the DMDS-TFET biosensor can be considered as one of the best proposed biosensors ever.   www.nature.com/scientificreports/ Although, from the beginning of the article to this point, we have considered fully filled cavities with the filling factor of 100% in all simulations, the existence of unfilled regions in the cavities may degrade the biosensor's performance 28 . To elucidate the impact of semi-filled cavities, we address four different configurations, plotted in Fig. 15. In Fig. 16a, the impact of Keratin biomolecule with a filling factor of 50% on the transfer characteristics of DMDS-TFET is depicted. It is evident that in case (b), the on-state current of the biosensor is close to the case in which the filling factor is 100%. While in cases (c) and (d), the drain current decreased significantly. This is mainly because, in these mentioned cases, the capacitive coupling of the gate with the tunneling junction has dropped. Figure 16b shows the impact of four semi-filled cases with the filling factor of 50% on the drain current sensitivity. In this figure, we have a S I ds = 3.78 × 10 4 for case (b), which is marginally close to the case with FF = 100%. At the same time, these values for cases (c) and (d) reach 63.54 and 28.91, respectively.

Conclusion
A novel TFET-based biosensor that benefits from dual doping-less tunneling junction is suggested. In this device, a U-shape channel connects source regions to an n + -doped drain region. Due to using silicon and SiO 2 in our biosensor, it is fully compatible with CMOS technology. Various neutral biomolecules, such as Uricase and Biotin, and charged DNA biomolecule were separately inserted into the cavities and the performance of the biosensor was evaluated by simulations. All the simulations were done by Silvaco ATLAS device simulator which had been calibrated by the valid data of the similar structure. We have shown that the role of trap-assisted tunneling, even in a doping-less tunneling junction, cannot be neglected. The impact of TAT on parameters like I on and subthreshold swing was calculated, and unignorably discrepancies compared with the cases in which TAT was not included have been observed. The realistic FOMs such as S I ds = 1.48 × 10 5 , and S SS = 0.61 illustrate that the performance of our biosensor is acceptable for high-sensitivity applications.

Data availability
The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.